The work of Daniel Kahneman, Richard Thaler and Dan Ariely have inspired many of the the frameworks we at Xabit. In this post, we share 3 books that we think are essential reads for anyone trying to do the same.
Just hearing the words “Corporate Structure” probably puts a lot of us to sleep. It sounds like one of those boring (sorry) topics that is the bread and butter of Lawyers (sorry, again!) but like raw eggs and kale for the rest of us. But what if we told you that we could give you a different, more interesting perspective? One that could potentially open your eyes into the several ways corporate structures (and the people behind them) reveal the reasons why they are built the way they are.
Interested? Great! We’ll try and give you such a perspective using Network Science, a less well known (but powerful) part of the Data Science world.
The Analyst’s dilemma is this: On the one hand, our brains are wired to rationalise the reasons for the current state of affairs all the time. We think “You can’t manage what you can’t measure” is a not a quote by a fellow analyst but rather gospel. On the other, 43% of Analysts [in the workforce] say they would be better than their boss at their boss’s job, which is classic Dunning–Kruger Effect at play. The boss is the boss, and we’re not them, for a reason, right?
The world around us is too vast and unpredictable for us to fully comprehend its complexity. To try and make sense of the complexity, our minds often rely on shortcuts and heuristics to make sense of complex information. Understanding the role of probability, and how our minds can be mislead in the face of it, is crucial in making better decisions.
Welcome to the final part of our three-part blog series, “A Retail Success Story.” In the previous two posts, we delved into how we helped our partners uncover previously hidden insights in their data and the obstacles we faced along the way. Now, we’ll be discussing what the analytic process led to: the development, operationalisation and growth of a new loyalty program.
In this three-part blog series, we will take a deep dive into the journey of the largest coffee chain in Nepal, Himalayan Java, and show how advanced data analytics unlocked hidden insights, leading to positive business outcomes and increased financial success. Join us as we delve into this exciting case study and discover how you too can revolutionize your business operations.
In our quest to make companies like Himalayan Java, the largest coffee chain in Nepal, more data-driven, we’ve learnt a thing or two about what retailers need to know when it comes to data and data analytics. For the benefit of other retailers considering a move into more data-driven operations, we thought we’d list out the lessons we’ve learnt so far.
At Xabit, we get a lot of calls from potential clients wanting to use our services to build dashboards for themselves and/or for their clients. We’d like for you to understand that there are three basic buildings blocks of any dashboard.
Over the past two years, our team has been working with companies in Nepal to connect the dots between their data and decisions. In our efforts, we’ve seen three cognitive biases that appear consistently across enterprises and hinder their ability to make better decisions.
People on the internet are asking OpenAI’s ChatGPT to do all sorts of things so we thought it might be a good idea to jump on the bandwagon and ask it questions about how well it knows it’s own foundations: data. Being a data analytics consultancy based in Nepal, we also thought it might be a good idea to ask it some specific questions about the industry in our home country.